This allows you to apply styles to specific rows or columns, without having to code that logic into your style function. props is a list of (attribute, value) tuples. Some of these will be addressed in the future. Questions: I would like to display a pandas dataframe with a given format using print() and the IPython display(). Use a dictionary to format specific columns. A tuple is treated as (row_indexer, column_indexer). Pandas matches those up with the CSS classes that identify each cell. You can apply conditional formatting, the visual styling of a DataFrame depending on the data within, by using the DataFrame.style property. CSS style (Cascading Style Sheets). These functions can be incrementally passed to the Styler which collects the styles before rendering. Let’s now generate a pivot table that has multiple columns of values: This creates a pivot table that looks like this: Now, let’s apply the background_gradient method: If we wanted to limit this to only one column, we can use the subset parameter, as shown below: Another illustrative way to add context to the size of a value in a column is to add color bars. Now we can use that custom styler. These require matplotlib, and we’ll use Seaborn to get a nice colormap. In this case, the cell’s style depends only on it’s own value. We can apply conditional formatting by using Dataframe.style property. Categorical data should have all the same formatting style, such as lower case. to_excel (writer, sheet_name = 'Sheet1') # Get the xlsxwriter workbook and worksheet objects. The best method to use depends on the context. This is a property that returns a Styler object, which has useful methods for formatting and displaying DataFrames. It’s necessary to display the DataFrame in the form of a table as it helps in proper and easy visualization of the data. These are placed in a ``